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Record W4402228379 · doi:10.1002/fer3.50

Increasing student science, technology, engineering and mathematics engagement through phyphox activities: Three practical examples

2024· article· en· W4402228379 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFuture in Educational Research · 2024
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMathematics educationStudent engagementScience and engineeringComputer scienceEngineering ethicsMathematicsEngineering

Abstract

fetched live from OpenAlex

Abstract For decades, educators in science, technology, engineering and mathematics (STEM) have strived to break the vicious circle of student disengagement at both secondary and post‐secondary levels. Despite the widespread availability of technologies like smartphones, STEM pedagogies have largely remained unchanged. Too often, students learn STEM theoretically with little hands‐on experience or opportunities to engage in authentic, research‐like activities. Modern smartphones can offer unprecedented opportunities for active STEM learning, but can also serve as distractors. Therefore, it is essential for teachers to acquire the pedagogical knowledge to harness these powerful tools effectively. This paper explores the potential of integrating smartphones into physics labs to enrich STEM learning. By leveraging smartphones' advanced capabilities for experimental design, data collection, and analysis, we have implemented a smartphone‐enhanced pedagogical approach in secondary physics classes and province‐wide Physics Olympics. We also implemented smartphone‐enhanced STEM pedagogies in teacher education. Our initial pilot study has yielded promising outcomes: enhanced student engagement in physics and deeper conceptual understanding. To advance this initiative, we propose structured teacher mentorship and professional development, empowering STEM educators to seamlessly integrate smartphones into their teaching. By embracing these modern educational tools, adopting evidence‐based pedagogical approaches, and supporting future and practicing educators we can make STEM learning more engaging and relevant for all students.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Bench or experimentallow
models splitAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.732
Threshold uncertainty score0.682

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.062
GPT teacher head0.414
Teacher spread0.351 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it